code: def sample_tensors(hid_size, num_layers):
      tensors = []
      for i in range(num_layers):
          tensors.append(torch.randn(hid_size, 3 * hid_size))
          tensors.append(torch.randn(3 * hid_size))
          tensors.append(torch.randn(3 * hid_size))
          tensors.append(torch.randn(hid_size, hid_size))
          tensors.append(torch.ones(hid_size))
          tensors.append(torch.zeros(hid_size))
          tensors.append(torch.randn(hid_size, 4 * hid_size))
          tensors.append(torch.randn(4 * hid_size))
          tensors.append(torch.ones(4 * hid_size))
          tensors.append(torch.randn(2, hid_size, hid_size, 2))
          tensors.append(torch.randn(hid_size))
          tensors.append(torch.randn(hid_size))
          tensors.append(torch.randn(hid_size))
      return tuple(tensors)
  
file_path: hivemind/benchmarks/benchmark_averaging.py
imports: import argparse
  import math
  import threading
  import time
  import torch
  import hivemind
  from hivemind.compression import Float16Compression
  from hivemind.utils.limits import increase_file_limit
  from hivemind.utils.logging import get_logger, use_hivemind_log_handler
label_desc: Use image node_id 1 for calling a global function with example usage: sample_tensors(hid_size, num_layers) and returns: tuple